DocumentCode :
4512
Title :
Cross-Layer Design of Distributed Sensing-Estimation With Quality Feedback— Part II: Myopic Schemes
Author :
Michelusi, Nicolo ; Mitra, Urbashi
Author_Institution :
Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA
Volume :
63
Issue :
5
fYear :
2015
fDate :
1-Mar-15
Firstpage :
1244
Lastpage :
1258
Abstract :
This two-part paper presents a feedback-based cross-layer framework for distributed sensing and estimation of a dynamic process by a wireless sensor network (WSN). Sensor nodes wirelessly communicate measurements to the fusion center (FC). Cross-layer factors such as packet collisions and the sensing-transmission costs are considered. Each SN adapts its sensing-transmission action based on its own local observation quality and the estimation quality feedback from the FC under cost constraints for each SN. In this second part, low-complexity myopic sensing-transmission policies (MPs) are designed to optimize a trade-off between performance and the cost incurred by each SN. The MP is computed in closed form for a coordinated scheme, whereas an iterative algorithm is presented for a decentralized one, which converges to a local optimum. The MP dictates that, when the estimation quality is poor, only the best SNs activate, otherwise all SNs remain idle to preserve energy. For both schemes, the threshold on the estimation quality below which the SNs remain idle is derived in closed form, and is shown to be independent of the number of channels. It is also proved that a single channel suffices for severely energy constrained WSNs. The proposed MPs are shown to yield near-optimal performance with respect to the optimal policy of Part I, also in this issue, at a fraction of the complexity, thus being more suitable for practical WSN deployments.
Keywords :
iterative methods; wireless sensor networks; WSN; cross-layer factors; distributed sensing-estimation cross-layer design; estimation quality; fusion center; iterative algorithm; myopic sensing-transmission policies; packet collisions; sensing-transmission costs; sensor nodes; wireless sensor network; Complexity theory; Estimation; Kalman filters; Sensors; Signal to noise ratio; Tin; Wireless sensor networks; Distributed estimation; Markov decision processes; cross-layer optimization; wireless networks;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2014.2388440
Filename :
7001684
Link To Document :
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